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Related Concept Videos

Sampling Methods: Sample Types01:18

Sampling Methods: Sample Types

Sampling materials are classified into three main types: solid, liquid, and gas.
Solid samples include a variety of substances, such as sediments from water bodies, soil, metals, and biological tissues. Two standard methods for extracting sediments from water bodies are grab sampling and piston coring. Grab sampling involves using a device to collect a discrete sediment sample from the bottom of a water body with minimal disturbance. Grab samples do not always represent the entire area due to...
Sampling Methods: Overview01:06

Sampling Methods: Overview

A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of sampling...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...

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Related Experiment Video

Updated: Jun 24, 2026

Sampling Soils in a Heterogeneous Research Plot
07:11

Sampling Soils in a Heterogeneous Research Plot

Published on: January 7, 2019

A comprehensive evaluation of soil sampling methods: advantages, limitations, and practical implications.

Wangfeng Ren1,2, Yueyuan Jiang3, Dengzhou Li1,2

  • 1Gansu Hengtong Road & Bridge Engineering Co., Ltd., Lanzhou, Gansu, 730070, China.

Environmental Monitoring and Assessment
|June 22, 2026
PubMed
Summary
This summary is machine-generated.

Effective soil sampling balances accuracy and cost. Stratified random sampling offers the best statistical reliability, while integrating spatial coverage methods enhances representativeness and efficiency for soil science research.

Keywords:
Conditioned Latin hypercube sampling (cLHS)Soil samplingSpatial coverageStratified random sampling

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Last Updated: Jun 24, 2026

Sampling Soils in a Heterogeneous Research Plot
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Area of Science:

  • Soil Science
  • Geospatial Analysis
  • Environmental Science

Background:

  • Accurate soil data is crucial for understanding spatial variability and overcoming logistical challenges.
  • Optimal soil sampling strategies must balance sample size, heterogeneity, and cost for reliable parameter estimation.
  • Real-world constraints like terrain and budget often impede ideal sampling plan implementation.

Purpose of the Study:

  • To review and synthesize major statistical and geometric soil sampling methods.
  • To provide practical guidance for designing effective soil sampling campaigns.
  • To identify strategies that maximize accuracy, representativeness, and cost-efficiency.

Main Methods:

  • Review of published studies on soil sampling techniques.
  • Analysis of statistical accuracy, spatial representativeness, and implementation feasibility.
  • Comparison of methods including simple random, grid-based, stratified random, spatial coverage, and conditioned Latin hypercube sampling (cLHS).

Main Results:

  • Stratified random sampling provides the highest statistical accuracy and reliability.
  • Spatial coverage methods excel in geographic representativeness.
  • Conditioned Latin hypercube sampling (cLHS) is a robust advanced method.
  • Integrating stratified random and spatial coverage techniques is the most effective overall strategy.

Conclusions:

  • The optimal soil sampling design balances statistical rigor, spatial coverage, and operational constraints.
  • Combining stratified random sampling with spatial coverage techniques maximizes scientific soundness and cost-efficiency.
  • This review offers guidance for planning soil sampling to support rigorous scientific analysis and geospatial applications.